Prior literature [1]} has shown that residual blocks-based approaches are employed as base networks by most state-of-the-art segmentation methods for 2D images. In this context, we propose a ResNet [2]} inspired architecture that accepts unordered 3D points directly as input and produces per-point segment or class labels. The network is developed with multi-layer perceptron (MLP) layers and ResBlocks. MLP layers perform convolution and ResBlocks facilitate better information flow to produce relevant features for segmentation and classification tasks. Hence, the main contribution of our network is to employ ResBlock along with MLP layers to produce features that are well-suited for segmentation.
